Empirical evidence shows that a significant proportion of analysts issue their forecasts at the time of an earnings announcement (Ivković and Jegadeesh 2004). These forecasts are commonly regarded as analyst interpretations of earnings news contained in the announcement (Schipper 1991). Although analytical studies suggest that market reaction to news from earnings announcement could be affected by analysts’ interpretation information (Kim and Verrecchia 1994, 1997), the vast majority of previous research has ignored whether and how these analysts’ interpreting forecasts affect the market reaction to the earnings announcements. Our empirical results show that sensitivity of trading volume reaction to earnings announcements is increasing in the number of announcement period analyst forecasts. The sensitivity of trading volume reaction is greater when there is small analyst forecast dispersion. We also find that stock return sensitivity is also increasing with the number of analyst forecasts. In general, our results suggests that analysts’ interpretation help disseminate new information contained in earnings announcement to the market.

We analyze an actuarial model of reverse mortgage programs in Korea. Our analyses provide a comparison between the reverse mortgage loans structured with constant monthly payments and those that make graduate monthly payments which are indexed to the growth rate of consumer prices. Using the total annual loan cost measure, we find that the graduate monthly payments approach is more efficient than the constant monthly payments approach. Our analyses also confirms that younger age cohorts are more sensitive to changes in their loan terms. We propose therefore that the terms of reverse mortgage programs should be structured more conservatively for the relatively younger borrower groups. The results provide useful information to reverse mortgage borrowers considering the various payment options, and pertinent guidelines for the future operation of reverse mortgage systems in Korea and elsewhere.

Standard choice models implicitly assume that consumers, in order to maximize their expected utilities, compare each of the alternatives in their choice sets in terms of all available attributes. Consumer-level utility functions are frequently taken as linear, and overwhelmingly so as compensatory. However, due to limitations in information process capacity, characteristics of choice task environment and other internal or external constraints, consumers may search for satisfying alternatives rather than optimal ones by invoking other non-compensatory heuristics which free them from arduous attribute-by-attribute comparison. The question arises as to how often these non-compensatory rules are applied, and whether researchers can detect them using only standard data sources. This study aims to address two main issues regarding consumers’ use of decision-rules and heuristics in the real world: (1) whether they are heterogeneous across consumers and (2) whether they are changing for individual consumers over time. To these ends, we extend the standard linear compensatory rule assumption to more faithfully capture dynamic heuristic usage for each consumer. There are three reference heuristics studied in this paper, the well-known linear compensatory, disjunctive and conjunctive rules. Conditional on this known set of possible heuristics, a dynamic heterogeneous hidden Markov mixture choice model is developed to capture heuristic dynamics at the individual-level. When estimated on detergent scanner data, the proposed model offers strong evidence supporting both heterogeneity and dynamics in heuristics usage.

It is customary in conjoint studies to introduce the same set of potential explanatory variables for each subject, so as best to allow any possible trade-offs to be made. However, this presumption can mask the possibility of some subjects’ considering only a subset of the presented attributes. Moreover, such subsets of relevant attributes can vary considerably across the population. This paper presents a model which allows researchers to identify relevant explanatory variables for each subject separately. This is accomplished via a solution to the well-known variable selection problem in the context of discrete choice models; the proposed solution can be widely applied throughout choice studies and in fact to other response types, such as ratings, direct paired comparisons, and ranks, with appropriate changes in likelihood function. When estimated on a choice-based conjoint data for dial-readout scale products, the proposed model is strongly preferred to the traditional random-effect specification for choice-based conjoint. A sizeable group of subjects, approximately 63%, were found to consider proper subsets of all attributes presented. There was a great deal of heterogeneity in attributes deemed relevant across subjects: the proportion of subjects who did not consider a given attribute among the six used in the study ranged from 17.4% to 41.3%. For those who did consider a given attribute, estimated attribute level part-worths were essentially identical for the proposed model and the traditional random-effect conjoint model; but this was not the case for non-considered attributes. In fact, the traditional model was found to suffer from systematic biases in aggregate part-worth magnitudes. Finally, and most important for marketing practice, allowing for the possibility that some subject may not consider particular attributes can lead to substantial design and revenue differences in supposedly ‘optimal’ products, at both the individual- and the aggregate-level.

This research examines the effects of pay differentials on financial performance and employee turnover in Korea by considering a critical employee-based factor: organizational identification. Incorporating tournament theory and social identity theory, authors theorize that pay differentials increase financial performance and employee turnover without considering employees’ organizational identification. If considered, however, whereas the positive effects of pay differentials on financial performance will be weaker, the effects on turnover will be stronger. Using a sample of Korean cross-industry firms, results show pay differentials have a positive influence on only financial performance. Also, as predicted, while the positive relationship between pay differentials and financial performance became weaker, the relationship with turnover became stronger when employees’ organizational identification is high. Theoretical and practical implications for strategic pay structures are discussed.

This paper summarizes recent studies in behavioral finance—particularly regarding market anomalies and investor behavior—that are not reconciled with the traditional finance paradigms. This paper differs from previous survey literature in several aspects. We introduce more recent papers in the field, more literature on behavioral corporate finance, and provide statistics on the recent trends that are explored in behavioral finance papers. We expand the research scope to studies on Korean financial markets, introduce specific funds using behavioral finance techniques, and discuss the challenges facing behavioral finance.

This study presents an evolutionary model of capability development and examines the role of strategic alliance in the evolution of organizational capabilities. In particular, this study begins with the observation that each alliance partner’s capability development co-evolves. This study contributes to the literature on capability development by showing the following: (1) a firm’s strategic alliance is ‘fitness-enhancing’ when its partners’ learning is ‘ineffective’; (2) without making additional efforts (i.e., in-house development), a firm is able to employ strategic alliances and to balance between exploitation and exploration to the extent that its (potential) partners are not effective learners; and (3) a firm is unlikely to balance between exploitation and exploration to the extent that its (potential) partners are effective learners.

Based on hand-collected non-GAAP earnings disclosures from 2001 to mid 2004, this paper finds that firms with communication motives, proxied by historically low returns-GAAP earnings relation, are more likely to disclose non-GAAP earnings in the post-Reg G period than in the pre- Reg G period. In contrast, firms with opportunistic motives, proxied by GAAP loss and negative GAAP EPS changes, are less likely to disclose non- GAAP earnings in the post-Reg G period than in the pre-Reg G period. With additional test results, the findings of this paper appear consistent with Congress’ and the SEC’s intervention in pro-forma reporting practices resulting in improvements in the quality of information provided in non- GAAP earnings disclosures.

We examine honesty and credible auditing in firm-investor relations in a repeated game of imperfect information, embedded in a general equilibrium framework. Informed auditors enhance credibility over a range of audit fees – despite the auditor’s incentive to collude – provided the probability of detection is imperfectly correlated across clients. Auditing can enhance growth especially for a relatively egalitarian distribution of wealth. We show that audit fees must be neither too high nor too low to enhance client credibility, highlight the role of mandatory audit fee disclosure, interpret international differences in shareholding patterns and uncover a possible rationale for audit industry concentration.

Small-medium enterprises (SMEs) play an important role in the economy worldwide and they normally need to borrow funds from financial institutions. Thus, an accurate credit risk model to predict the probability that these firms might be bankrupt and cannot pay back the loans on time is very crucial. However, the studies based on SME data are very rare especially for those in emerging markets. This study develops the SME models by employing both the Multivariate Discriminant Analysis (MDA) and Logistic Regression Analysis (Logit) model in predicting bankruptcy of SMEs in Thailand. The samples cover the period 2000 – 2010. The result shows that the Logit model gives higher predictive accuracy level at 85.5 percent for out-of-sample test. Moreover, the combined forecasts of bankruptcy firms from both MDA and Logit models could help achieve even higher predictive accuracy level.